Enhanced Grey Wolf Optimizer Algorithm (I-GWO)

The I-GWO algorithm incorporates an innovative movement strategy called Dimension Learning-Based Hunting (DLH), which mimics individual hunting behaviors observed in natural wolf packs. DLH constructs unique neighborhoods for each wolf using varied approaches, enabling information sharing among neighboring wolves. The dimension learning mechanism within DLH enhances the balance between local and global search capabilities while maintaining population diversity. The provided code demonstrates I-GWO implementation on benchmark test functions, featuring key components like position updating, fitness evaluation, and neighborhood construction functions.